A Feature Aggregation Module combines the disentangled representations from the previous two steps.
The aggregation module combines any given pair of style and content embeddings to produce one embedding.
For the experiments that follow, we use the LayerNorm [1]} operation with learnable parameters as our feature aggregation module.
There are four different possible pairs that can be the input to our model, since there are two factors of variation (style and content) and two domains (synthetic and real-life).
For any given input pair, the output feature representation can be thought as the content in the style of the specified domain.
